Germany asleep at the wheel?

Artificial intelligence and machine learning are set to disrupt the status quo in the automotive industry and other areas where Germany traditionally excels. How well prepared is Europe’s car manufacturing powerhouse for the future of autonomous cars?

Moritz Mueller-Freitag
Published in
22 min readJan 30, 2017


A self-driving car prototype by German automobile manufacturer Mercedes-Benz. Source: Daimler


The German automotive sector is a prime candidate to be rethought through the lens of artificial intelligence (AI). In this article, I will outline why autonomous cars will drive a paradigm shift in the auto industry (part I), argue that the problem for German car manufacturers is their old-school engineering culture, not a lack of innovation (part II), and examine counter models to Germany’s current approach to self-driving cars (part III). I will conclude with a number of suggestions for improvement that would, in my view, put the German car industry back on track.

I. Why Germany’s auto industry is at risk

The automotive industry is one of the most important industrial sectors in Germany. It employs nearly 800,000 people, produces roughly 15 million passenger cars per year and generates more than €400 billion in annual revenues. More than 70% of all premium brand vehicles produced worldwide are manufactured by German OEMs — in particular by the three industry leaders BMW, Mercedes and Audi. The auto sector is also a major driver of research and development (R&D), accounting for about 40% of the economy’s R&D budget. Germany is a car-crazy nation and these numbers are testament to the fact that the industry remains absolutely central to the country’s export-oriented economy.

Advanced sensor systems of a highly automated car. Source: Andreessen Horowitz

It is therefore all the more disturbing that the German automotive sector has come under immense pressure to develop the two key technologies of the future: electric mobility (i.e. cars that are propelled by electric motors instead of internal combustion engines) and autonomous driving (i.e. cars that are capable of sensing their environment and navigating without human input). Germany’s proud carmakers missed out on the first trend and are currently overrun by Tesla in the race to build driverless cars. While the failure to lead in electric mobility is by itself excruciating, being left behind by the self-driving revolution could put the future of Germany’s most important economic sector at risk.

Autonomous driving is a paradigm shift that will likely change everything for traditional carmakers. The gradual transition towards battery technology could soon destabilize the traditional automotive supply chain, which has its emphasis on components like the combustion engine or gearbox. Even though the dismantling of this value chain poses a threat to carmakers, electrification does not drastically change the meaning of the word “car” in the eyes of consumers. In contrast, the trend towards greater autonomy will fundamentally change what cars are. As Benedict Evans has observed, the word “car” will mean something completely different in the age of autonomy — much like the word “phone” was transformed after the launch of the iPhone. Autonomous vehicles will throw existing business models into question and challenge the very notion of car ownership. If the car is in the process of being reimagined, so too must their creators.

As if this wasn’t enough, high-tech newcomers like Tesla, Uber, Google, Nvidia and tons of startups are hacking away at the market. The march towards electrically-powered autonomous vehicles has significantly lowered the barriers to entering the auto business for two major reasons. First, batteries will be a commodity once production is up and running, as will be the hardware sensors — cameras, radar, lidar, ultrasonic sensors — that are prerequisites for building self-driving cars. Second, electric vehicles will have significantly fewer moving parts than cars powered by an internal combustion engine, which could change the whole basis of competition. Note that this does not imply that car making is becoming trivial, as demonstrated by Apple’s attempts to launch its own car project. As the key hardware components of autonomous cars become cheaper and increasingly commoditized, most of the car’s value will shift to its software components.

The shift of value away from predominantly hardware (the car’s “body”) to software (the car’s “brain”) creates an existential threat for traditional carmakers. Software is not a core competence of auto OEMs, even though it has long played a role in carmaking. According to Manfred Broy, a Professor of Computer Science at the Technical University Munich, a modern premium-class car already “contains close to 100 million lines of software code” — much more than a modern airplane or fighter jet. However, writing firmware to control the car’s air conditioning is very different from teaching a car to drive itself. For example, autonomous vehicles use incredibly sophisticated machine learning algorithms to interpret the car’s surroundings and instruct the various hardware components to perform the necessary actions. Self-driving cars are also massive data-generation machines, generating one terabyte of data per hour by some estimates. Not to mention the emerging content opportunities that arise when software takes over the car. Excelling at these areas of software development requires an entirely new skill- and mindset from the vantage point of auto companies.

It is not only car manufacturers who are in need of software skills. The paradigm shift in the auto industry is also impacting car parts suppliers who have undergone a wave of consolidation in the past two years. According to data compiled by Bloomberg, the deal volume among automotive suppliers totaled $74.4 billion in 2015 and the first half of 2016 — far exceeding the $17.7 billion spent on average in the previous ten years. In particular, the three large German tier-one suppliers have been gearing up on software: In 2015, ZF Friedrichshafen acquired U.S. software provider TRW Automotive for $12.4 billion. The same year, Continental took over the software division of Elektrobit Automotive for $660 million. Bosch meanwhile acquired ZF Lenksysteme, a supplier of advanced steering systems, and more recently ITK Engineering, a software company employing more than 800 people. Both the volume and diversity of these deals are striking but support the view that incumbent parts suppliers must also retool their software teams.

The self-driving revolution raises important questions for the entire manufacturing sector. Why are Germany’s automobile companies perceived to be lagging behind? How is it possible that German consumers suddenly put more trust in Tesla than in domestic carmakers when it comes to putting a market-ready autonomous vehicle on the road? And why is the German auto juggernaut moving so incredibly slowly? At first glance, one has the impression that the likes of Mercedes or BMW could easily crush Tesla or Uber in autonomous driving. Both camps have similar technology when it comes to semi-autonomous driving systems (see here and here). Mercedes and BMW have many more cars on the road than Tesla and Uber though, which they could leverage to build up a formidable dataset for self-driving cars. Why then aren’t they charging ahead with their powerful brands, century-old experience and massive development budgets?

II. Germany’s old-school engineering culture at fault

To answer this question, it is useful to rewind the clock. In a marketplace where Tesla’s Autopilot currently dominates the narrative, it is easily overlooked that Germany was once at the vanguard of autonomous driving. Thirty years ago, Mercedes-Benz began cooperating with Ernst Dickmanns — a little known professor at the Bundeswehr University Munich — to build a self-driving car prototype that could react autonomously in real time to road and traffic conditions. In 1986, their proof-of-concept car VaMoRs made its first autonomous drive on a closed strip of the German Autobahn. The 5-tonne Mercedes van — chosen because its huge volume could house the necessary computer equipment — was outfitted with cameras, other sensors and a range of 8-bit Intel microprocessors. A year later, VaMoRs was already able to drive autonomously in highway traffic at speeds of up to 96 km/h (60 mph).

Ernst Dickmann’s early prototype VaMoRs, a refined 5-tonne Mercedes van. Source: Computer History Museum

Encouraged by these early successes, the intergovernmental research center Eureka launched the Prometheus Project in 1986, together with the Bundeswehr University, Mercedes-Benz and several other car manufacturers. The goal of the well-funded R&D project was to advance autonomous driving technologies by developing more sophisticated computer vision software. To deal with the incredibly slow computing power of the time, Dickmanns came up with a technique that could estimate spatial positioning and velocity without the need to store previously captured images (“4D-approach to dynamic machine vision”). He also experimented with saccadic gaze control for visual attention focusing and used probabilistic approaches like Kalman filters. Around the same time, researchers at Carnegie Mellon University pioneered the use of neural networks to steer autonomous vehicles.

The Prometheus project achieved its first landmark result in 1994 when two re-engineered Mercedes W140 S-Class vehicles (VaMP and VITA-2) drove autonomously for more than 1,000 kilometers (621 miles) on France’s busiest highway, the Autoroute A1 that connects Paris with Lille. The second landmark was a 1,750 kilometer round trip (1,087 miles) from Munich (Germany) to Odense (Denmark) in 1995. This time around, the cars were equipped with 18 cameras and 70 parallelized microprocessors that performed 850 million operations per second. In comparison, a 2016 Nvidia Titan X GPU gives you a compute of 11 trillion floating-point operations per second. In the light of the available hardware, the results of both trips were impressive. The Mercedes prototypes were able to drive in heavy traffic for long distances without human intervention by using computer vision to recognize, avoid and overtake other cars. The test vehicles achieved speeds of up to 185 km/h (115 mph) and performed autonomous lane changes at over 140 km/h (87 mph). At one point, VaMP drove autonomously for 158 kilometers (98 miles) without a single human intervention.

Left: A self-driving Mercedes S-Class driving on the German Autobahn in 1994. Right: A self-driving Uber driving in Pittsburgh in 2016. Source: Computer History Museum (left) and TechCrunch (right)

The fruitful cooperation between Ernst Dickmanns and Mercedes-Benz found its peak over 20 years ago, long before Tesla introduced Autopilot and Uber began their much-hailed test run of self-driving cars in Pittsburgh. On the one hand, it demonstrates that truly disruptive technologies usually take a long time to become an “overnight success.” Apple did not invent the smartphone but eventually reaped most of the profits with the iPhone. On the other hand, this tale is a clear indication that the problem for German premium automakers is not a lack of innovation. Germany accounts for 58% of worldwide registered patents for autonomous driving and spends a ton of money on research (Volkswagen, Daimler and BMW together spend some €22 billion on R&D per year). Rather, it is the overly risk-averse mentality of German car executives that turns out to be the problem. Instead of increasing their technological lead in self-driving cars, the VaMP research car disappeared into the Deutsche Museum in Munich. In the years that followed, German carmakers went on to focus their research on more modest topics like driver assistance (e.g. Automatic Emergency Braking or basic Lane Keeping Assistance).

Today, just as twenty years ago, the strategic dilemma for German carmakers is that they feel that fully autonomous cars might never fit with the self-image of their customer base. This is particularly problematic for the premium automakers. Their brands are inextricably linked to the relationship between the car and the driver — a fact best reflected in BMW’s longtime marketing slogan Freude am Fahren, which translates to “the joy of driving.” The core value proposition of BMW, Mercedes and Audi is Fahrvergnügen (driving pleasure), not mobility. On top of this, carmakers are rightfully cautious when it comes to their reputation. When cars drive themselves, the slightest software glitch can lead to catastrophic results. As VW learned during its emissions-cheating scandal, trust is extremely fragile. It takes years to build a reputation, but only seconds to destroy it beyond repair.

At the technical level, carmakers have also been held back by the old-school thinking of senior management, especially at middle management level. Many auto engineers in Germany were long convinced, for example, that camera-based tasks like scene understanding could only be solved with traditional computer vision approaches (e.g. thresholding and image segmentation combined with decision trees or support vector machines). Traditional computer vision relies heavily on manual feature engineering and selection, whereas the defining characteristic of deep learning is representation learning, i.e. letting algorithms learn the relevant features themselves. Until recently, the prevalent opinion in the car industry was that deep learning was not suited for autonomous cars due to a lack of training data, and the fact that neural networks can be slow to train and difficult to debug. Forward-looking engineers who wanted to switch to deep learning often had to quarrel with senior colleagues who had been working on hand-crafted approaches or unsuccessfully tried neural networks at one point in their careers. This generation conflict might explain why so many startups have popped up in the autonomous driving space recently. Fast-moving startups can start with a blank piece of paper and are not burdened by any legacy approaches imposed by old-school leadership.

With the dawn of the machine learning era, traditional carmakers face enormous difficulties to adjust to this new software model. In particular, they are struggling to attract enough of the right talent. Competition is extremely fierce for specialists in self-driving car development, as demonstrated by GM’s acquisition of Cruise Automation (40 employees) for more than a $1 billion and Uber’s purchase of Otto (91 employees) for $680 million. As a result, the going rate for self-driving talent can be as high as $10 million per person according to Sebastian Thrun. This is an inconceivably high number in the eyes of German car executives. What stands out here is that tech companies in the U.S. tend to offer much higher salaries than their German counterparts. To make matters worse, German universities are not producing enough software engineers and machine learning specialists to satisfy the talent needs of the car industry. Klaus Fröhlich, the Head of Development at BMW, was quoted last year that his company needs to get “manpower equivalent to another 15,000 to 20,000 people from partnerships with suppliers and elsewhere.”

Alexander Dobrindt, Germany’s Minister of Transport, at the IAA. Source: BMVI

There is also a more systemic reason why Germany has lost its lead in autonomous driving: its outdated regulatory framework. Under the Straßenverkehrsordnung, or German Road Traffic Regulation, (semi-)autonomous driving systems that allow the driver to perform tasks other than driving are currently not admissible. As a result, German manufacturers have been disincentivized to ship advanced driverless features to customers. They were also compelled in recent years to do much of their testing in the United States, which hashed out the legalities of self-driving cars much quicker. Audi, BMW, Mercedes, VW and Bosch concentrated some of their best people in the U.S. to test their cars in road traffic, exacerbating the brain drain of German computer scientists (tech companies regularly poach auto specialists from traditional carmakers). Only recently did the government allow car manufacturers to test their prototypes under real-world conditions on the A9 motorway in Bavaria. Germany is now, finally, rushing to amend its traffic laws to permit self-driving cars. However, the proposed traffic code reform is not expected to be enacted before the federal election in autumn 2017.

On a final note, Germany’s incredibly slow start into the race for self-driving cars might also be attributed to the fact that the country is perceived to be good at pure research but bad at translating technical innovations into marketable businesses. An often-cited example is the invention of the MP3 music format. German researchers at the Fraunhofer-Gesellschaft developed digital compression in the 1980s but it was Asian companies that brought MP3 players onto the market and Apple who eventually created the iPod. The type of innovation where Germany traditionally excels at involves infusing old products and processes with new ideas and capabilities. The German manufacturing industry has long thrived on this type of innovation. It approaches fundamental disruptions with caution. This mentality has served German companies well in the past as many have operated in obscure industrial niches that don’t face existential threats from innovation at the edges. Thriving in a hypercompetitive mass market like autonomous vehicles will, however, require a change of mindset to say the least.

So if autonomous driving requires a cultural shift, what is the counter model to the German car industry? In my opinion it is Tesla, a tech upstart that has claimed the lion-share of media frenzy surrounding self-driving cars. This is despite the company having an insignificant market share globally in the premium car segment. To understand the cultural shift that carmakers have yet to make, it helps to take an intensive look at Tesla’s rollout strategy and examine how the traditional auto industry has reacted to the speed and ambitious risk-taking of Tesla’s founder Elon Musk.

III. How Tesla came to dominate the narrative around self-driving cars

The race to get driverless vehicles on the roads really only intensified in October 2015 when Tesla launched its heavily promoted Autopilot feature. Autopilot is an advanced driver-assistance system (ADAS) that automatically handles steering, braking and acceleration of the Tesla Model S when it is engaged. The system is what the NHTSA and the German Transportation Ministry classify as a Level 2 semi-autonomous driving system, a combination of Lane Keeping Assist and Adaptive Cruise Control. The Tesla Model S is by no means a fully self-driving car (Level 4/5). The car requires constant monitoring by the driver who must be able to assume full control of the vehicle at any time. Although the feature was labeled “public beta” upon launch, Tesla felt that the system was safe for public use, assuming that drivers proceed with caution and due attention when it is engaged.

Unsurprisingly, the traditional car companies see things quite differently. The general consensus among German automakers is that Tesla rolled out its “experimental technology” too soon. Talk to any German car executives these days and they will likely point to Tesla’s self-driving road death in May 2016, arguing that Germany’s go-slow approach may turn out to save their customers’ lives. It is worth noting, however, that installing Autopilot shows a 40% crash rate reduction and is potentially saving more lives than traditional cars do. The critical view of car execs is mirrored by the increasingly technophobic media coverage of self-driving cars in Germany. Der Spiegel, for example, has claimed that Tesla is using “human guinea pigs” to test their cars, while Süddeutsche Zeitung dismissed Autopilot as “the great, dangerous Tesla show.”

German news magazine Der Spiegel warning about the dangers of progress in 1964 (left), 1978 (middle) and 2016 (right)

These interpretations gravely miss the point. They take the word “beta” literally, translating it to “premature” or “dangerous”. In reality, the word “beta” is best translated as “not perfect”, cautioning users to not get too comfortable with the system. Offering Autopilot as a “beta” technology emphasized the fact that the software was expected to dramatically improve over time by leveraging the power of large-scale, distributed machine learning (“Fleet Learning”) and regular over-the-air software updates.

The results of Tesla’s radical approach are so far astounding: Over a period of just 12 months, the company has collected more than 1.3 billion miles (2.1 billion kilometers) of camera/radar data from roughly 70,000 vehicles that are equipped with Autopilot. To be clear, those are miles driven in Model S cars with Autopilot hardware, not only miles when Autopilot was engaged. In comparison, it took Google’s Waymo seven years to cover 2 million real-world miles (3.2 million kilometers) with its fleet of 58 robo-cars, which are powered mainly by a lidar array on top of the vehicle. Tesla’s approach of crowdsourcing real-world data and continuously improving its machine learning models in real time stands in stark contrast to the approach of Google and German OEMs: localized testing with a limited fleet of test vehicles. An example is BMW’s latest announcement at the Consumer Electronics Show (CES) in Las Vegas. Together with Intel and Mobileye, they plan deploy a fleet of 40 self-driving test cars in the second half of 2017.

Tesla’s Autopilot, a level 2 semi-autonomous driving system, in action. Source: Tesla Motors

These different data collection strategies are creating a growing chasm between Tesla and all other traditional carmakers. The state of the art in machine learning is to expose deep neural networks to large quantities of labeled training data and then incrementally improve the results. Tesla, for example, is using neural networks for its vision, sonar and radar processing software. Building autonomous cars with deep learning requires a large training dataset, ideally gathered from diverse road and weather conditions and from a large variety of real-world locations and driving behaviors. In today’s hypercompetitive market environment, speed is the key, so whoever can put more cars on the road to collect and label the necessary data will have an edge. As with other machine learning applications, data will eventually become one of the primary sources of competitive advantage for car makers. A bigger fleet of self-driving cars will provide access to more data, which in turn makes software performance improvements possible. This could one day become extremely attractive for ride-sharing companies if they can jumpstart a data network effect (where more users → more data → better self-driving capabilities → higher safety → more users).

The speed at which Tesla has updated the functionality of Autopilot over the past months is truly impressive. According to Alex Roy, editor-at-large for The Drive, Tesla’s Fleet Learning “has yielded more software improvements in the 11 months since Autopilot’s initial release than most manufacturers achieve in a traditional 3–5 year model cycle.” Tesla operates at quite a different speed to the car industry and incumbents clearly have trouble adjusting to the new pace. In the words of a Mercedes official: “You can’t compare a 130-year-old company shaped by German engineering ingenuity with a startup from Silicon Valley. It’s a different approach.”

Counterintuitively, what is holding back German auto companies is their emphasis on perfectionism. It is no coincidence that the guiding principle of Mercedes-Benz is Das Beste oder nichts, or “the best or nothing”. However, as Christoph Keese has observed in his latest book Silicon Germany, the opposite of perfectionism is not carelessness. Instead, it means bringing products to market before they are perfect (as long as they are save) and then rapidly iterating in the wild to improve them. This approach to development is even more relevant in the context of machine learning, as described by Mike Schuster, a German research scientist at Google Brain: “The machine-learning mechanism is never perfect. You need to train, and at some point you have to stop. That’s the very painful nature of this whole system. It’s hard for some people. It’s a little bit an art — where you put your brush to make it nice. It comes from just doing it. Some people are better, some worse.” To quote Voltaire, “le mieux est l’ennemi du bien.”

One might argue that Germany is a nation of machine builders, not of software engineers. Guided by the mindset of mechanical engineers, German automakers approach the design of software components the same way they would design an engine component. As a result, Mercedes’ own version of Autopilot, branded as Drive Pilot, “feels like the very best hardware solution to a problem that requires artful software thinking,” as Alex Roy so aptly put it. Although the auto critic called the Mercedes E-class “probably the world’s best ADAS”, he ended up publishing a devastating critique of Drive Pilot, arguing that “it drove like a drunk ten year old, fighting for the wheel with a drunk fourteen year old.” The brittleness of Mercedes’ software can probably be explained, in large part, to the lack of real-world training data. All evidence suggests that Mercedes does not even come close to Tesla’s dataset. It can also be because traditional car manufacturers have independent product teams writing software to control their own parts, thus creating software silos. By contrast, Tesla really excels at bringing all software systems together under the roof within a central operating system that controls every function of the car. This is a major advantage to becoming a full-stack car manufacturer vs. a third-party vendor of driverless car software.

The contrast between Tesla and German OEMs might be best explained by drawing an analogy to the different methodologies of software development: While Tesla’s Autopilot is based on an iterative approach (agile software development), the autonomous driving projects of German companies seem to follow a strictly linear, sequential process (waterfall development). As a result of this mindset, Mercedes does not plan to update Drive Pilot during service (hence no “Fleet learning”). They do not plan to leverage their existing fleet of vehicles to operate Drive Pilot in something like “shadow mode” so the software can be improved offline after downloading data from live driving environments. Neither do BMW or Audi with their planned semi-autonomous driving suites. German car executives believe they can afford to watch Tesla “experiment” with Autopilot before entering the self-driving race once their own technology is ready. I disagree.

Germany’s carmakers claim they are totally committed to winning the driverless car race, but in reality they are trying to accelerate with the handbrake still firmly engaged. While Tesla’s Level 2 system goes on to improve in the hands of its users (and is by now nearly a Level 3 car), Germany’s premium carmakers risk falling further behind. Elon Musk expects the first fully autonomous Tesla to be ready by 2018 (regulatory approval may take 2–3 more years thereafter). German OEMs anticipate fully self-driving cars much later. BMW, for example, reckons that a fully autonomous version of the iNext will be market-ready in 2025. Tesla is thus in the pole position to go autonomous first. That would send ripples through the auto industry because AI systems are, by their very nature, capable of delivering accelerating returns. Can the Germans compete against a rival like Tesla? I am convinced they can if they change their mindsets.


Not all is lost for the German automotive industry. In fact, there are reasons to be optimistic. Germany’s premium carmakers still have control over the industry and direct relationships with consumers. They also enjoy some advantages over tech rivals thanks to their size and operational efficiency. Indeed, it remains an incredibly difficult task to reliably manufacture high-quality cars at mass scale. This is something the Germans are really good at. Tesla, on the other hand, has yet to “cross the chasm”, i.e. transform from serving a base of predominantly early adopters to becoming a profitable full-scale producer as outlined in their 10-year master plan. Nonetheless, there is absolutely no reason for the Germans to be overconfident or complacent. Blackberry and Nokia were in similar positions on the eve of the smartphone age. To avoid their fate, the following things have to change:

  • Strategy: I believe that German OEMs must adopt Tesla’s iterative approach to the development of autonomous driving systems, of course without compromising their reputation for quality and safety. They should equip all their cars with networked sensors and, more importantly, gather as much real-world driving data as possible. There is no question that every single one of them could still catch up to Tesla. After all, they can put many more cars on the road than the American automaker. They must, however, begin to take data gathering seriously. Fully autonomous cars will most likely be built on the shoulders of crowdsourced data generated by networked semi-autonomous driving suites, not via localized testing and software updates over traditional 3–5 year model cycles. This is a strategy adjustment that Daimler, BMW or Volkswagen have yet to take.
  • Culture: Germany’s carmakers suffer from an old-school engineering culture that is overly hierarchical, discourages risk-taking and is rooted in a mechanical engineering mindset. Competing in the digitized automotive future requires a cultural change throughout the car industry. Fortunately, some car companies are sending encouraging signals, like Daimler whose forward-looking CEO, Dieter Zetsche, is working towards implementing a swarm organization to break down the company’s hierarchy. Others are lagging behind with their digital transformation, like VW which is still grappling with the aftermath of Dieselgate.
  • Capital allocation: Electric mobility and autonomous driving are shifting the car’s value away from the powertrain (hardware) to the operating system (software). As a result, carmakers should focus an increasing amount of Capex and R&D on software development. This would, admittedly, mark a break with the past: OEMs have historically spent vast amounts of capital and R&D on proprietary hardware components because this is where their core competence lies. A shift of focus towards software could be overdue though. In recent years, many automotive hardware innovations have been either duplicative across the industry or not really discernible to customers (see slide 10 of this presentation by Sergio Marchionne). If Germany’s carmakers plan to survive the autonomous future, they must develop autonomous driving capabilities in-house (ideally in Germany) and consequently equip their R&D staff predominantly with computer programmers. These structural changes need not be impossibly hard to enforce but they are necessary if carmakers want to avoid becoming the Foxconn of carmakers.
  • Branding: The automotive market is split between mobility customers (those who use ride-sharing services) and driving customers (those who still purchase cars). In the next few years, autonomous driving will most likely accelerate the transition from car ownership to shared mobility. This is a problem for Germany’s luxury carmakers as their brands cater predominantly to driving customers. In my eyes, there is really only one way to solve this conflict: car manufacturers have to spin off their mobility products under sub-brands and position these brands for the autonomous future. Mercedes and BMW have taken first steps into that direction with subsidiaries like car2go or DriveNow. However, they are hardly consistent in their branding strategies, as shown by the BMW i sub-brand (the i3 is a mobility play, the i8 is for driving pleasure). Without clear brand positioning, carmakers risk brand dilution.
  • Regulation: Autonomous driving is not a field where the government can play catch-up. Unless the pace of regulatory change picks up, Germany risks falling behind in setting global standards for an industry that is strategically important for the economy. Fortunately, first steps have been made in recent months towards a reform of the German traffic code. First, the Ministry of Transport launched an expert roundtable on automatic driving and recently published a white paper called “Strategy for Automated and Connected Driving”. Second, the Ministry of Economic Affairs launched a research project devoted to the standardization of testing and admission for highly automated driver assistance systems. However, these initiatives will hardly be enough to make Germany a front-runner in regulatory matters. No traffic code reform is to be expected before the 2017 federal election and when the reform arrives, it will most likely be half-baked.

Taken together, I believe that the disruption of the car industry holds important lessons for other manufacturing sectors. I am convinced that the self-driving revolution is a harbinger of things to come for corporate Germany. My contention is that many other industries could soon experience their own “Tesla moment” — i.e. an aggressive data-driven tech company like Tesla intruding a hardware market that was traditionally controlled by just a handful of established companies. For Germany, whose manufacturing sector comprises 22.5% of GDP and employs 15 million people, this will pose a challenge. Benedict Evans, among others, is convinced that “it is easier for software to enter other industries than for other industries to hire software people.” As software evolves from a supplementary function in machines to its key component, this shift could pose an existential threat to some Mittelstand companies and large-sized enterprises. I am nonetheless hopeful and optimistic that Germany will overcome the challenges posed by AI.

Thanks to Nathan Benaich, Thibault Févry, Hendrik Brackmann, Karl Kurzer, Philipp Graf and Christian David Martón for proof reading this article.



Moritz Mueller-Freitag